- Problem Set 1
- Linear Classifiers (Logistic Regression and GDA)
- Incomplete, Positive-only Labels
- Poisson Regression
- Convexity of Generalized Linear Models
- Locally Weighted Linear Regression
- Problem Set 2
- Logistic Regression: Training Stability
- Model Calibration
- Bayesian Interpretation of Regularization
- Constructing Kernels
- Kernelizing the Perceptron
- Spam Classification
- Problem Set 3
- A Simple Neural Network
- KL Divergence and Maximum Likelihood
- KL Divergence, Fisher Information, and the Natural Gradient
- Semi-supervised EM
- K-means for Compression
- Problem Set 4
- Neural Networks: MNIST Image Classification
- Off Policy Evaluation and Causal Inference
- PCA
- Independent Components Analysis
- Markov Decision Processes